Truty, Mark J.

Portal hypertension is a key factor in posthepatectomy liver failure (PHLF). While preoperative liver function tests like APRI+ALBI assess liver function, they only partially reflect portal hypertension severity. Elevated von Willebrand factor antigen (vWF-Ag) indicates endothelial dysregulation and correlates with portal hypertension. Combining vWF-Ag with APRI+ALBI may enhance PHLF prediction. A total of 534 patients who underwent liver resection at Mayo Clinic Rochester (2020–2024) were analyzed for PHLF incidence, postoperative morbidity, and 90-day mortality. Predictive probability for PHLF was assessed using receiver operating characteristic analysis and validated in an external Austrian cohort of 283 patients (2008–2017). vWF-Ag was then integrated into the existing APRI+ALBI multivariable model. PHLF grade B/C was reported in 56 patients (10.5%). The 90-day mortality rate was 0.9% (n=5). Using 2 cutoffs (182%, 240%), vWF-Ag was found to be an independent predictive factor for PHLF grade B/C that remained statistically significant upon multivariable analysis. The combination of preoperative APRI+ALBI and vWF-Ag increased the positive predictive value to 27.9% for PHLF grade B/C in the APRI+ALBI high and vWF-Ag high group. Incorporation of vWF-Ag into the established APRI+ALBI-based multivariable model revealed a superior AUC of 0.772 for PHLF risk stratification and was successfully validated in an independent cohort (AUC=0.834). The combination of vWF-Ag with APRI+ALBI demonstrates a favorable predictive potential for PHLF risk assessment. We provide 2 pathways for clinical assessment: (1) a cutoff-based system and (2) a more complex multivariable model that can be calculated in a specifically designed smartphone application.

Abstract NF-κB signaling can be subdivided into canonical and noncanonical pathways, culminating in the transcriptional activity of RELA and RELB, respectively. However, the upstream signals that activate these transcription factors and their specific regulatory roles in pancreatic ductal adenocarcinoma (PDAC) remain incompletely understood. Here, we demonstrate that TNFα is the primary activator of canonical NF-κB signaling via RELA, while TWEAK engages noncanonical signaling through RELB in PDAC. Using transcriptome-wide gene expression, genome-wide occupancy profiling, and epigenome mapping, we delineate distinct temporal dynamics and regulatory activities of each pathway. Single-cell RNA-seq analysis and multiplex immunofluorescence staining in primary PDAC samples further reveal extensive and distinct interactions with components of the tumor microenvironment. Motif analysis of RELA- and RELB-bound regions uncovers a particularly strong association of RELB with AP1 elements. Notably, while RELA binds to both open and closed chromatin, RELB exclusively occupies regions with pre-existing chromatin accessibility and AP1 co-binding. Collectively, these findings underscore the distinct and complementary roles of TNFα and TWEAK in PDAC, with TNFα engaging a broader transcriptional program via RELA and TWEAK selectively targeting genes marked by accessible chromatin and epigenetic activity. This study provides critical insights into the regulatory dynamics of NF-κB signaling in PDAC, highlighting unique and complementary functions of RELA and RELB in modulating downstream gene expression. Significance TNFα and TWEAK selectively activate RELA and RELB in pancreatic cancer, revealing that RELB-driven noncanonical signaling depends on chromatin accessibility and AP-1, uncovering distinct, targetable epigenetic dependencies from canonical signaling. Graphical abstract

e14551 Background: Extracellular vesicles (EVs) are microscopic particles released by all cell types with a distinct fingerprint reflecting the cell of origin. In patients with prostate cancer, PSMA+EVs have been associated with clinical outcomes. In this study, we investigated tumor-derived EVs (tdEVs) using the tumor-specific CD326 marker as a novel biomarker for pancreas ductal adenocarcinoma (PDAC). Methods: The study included patients with localized PDAC treated on a prospective phase 2 clinical trial evaluating a 15-fraction chemoradiation (CRT) regimen. Blood samples were obtained after induction chemotherapy (C) (prior to CRT) and in follow-up. 20 healthy controls were included. High-resolution flow cytometry enumerated levels of fluorescent antibody labelled tumor-specific CD326 (EpCAM)-positive EVs. All samples were de-identified and randomly coded. The technologist responsible for EV analysis was blinded to patient information and clinical data. Each sample was run in triplicate. Flow cytometry data was analyzed with custom-built software to automatically determine and apply gates and generate reports with scatter plots and EV concentrations for each combination of markers and data summaries. Results: tdEV data were analyzed from 70 patients with pre-CRT samples. Patients had mean age 66 years (9.0 standard deviation), with 60% ECOG 0, 43% borderline resectable and 48% locally advanced. Patients had a median 8 cycles of C prior to CRT, and 70% proceeded to surgery after CRT. 2-year overall survival (OS) was 54%. Patient samples showed elevated total CD326 EVs compared to the healthy controls (p = 0.0005). Furthermore, patient CD326 EVs revealed two distinct subpopulations: small (120-1,000nm) and large (1,000-3,000nm) size CD326 EVs. No large CD326 subpopulation was found in healthy donor samples. Higher pre-CRT levels of total CD326 and large CD326 EVs were associated with decreased progression-free survival (PFS) (hazard ratio (HR) = 2.38, p = 0.013 and HR = 2.017, p = 0.021) on multivariable models with surgical resection. Resection was associated with increased PFS (p < 0.0001), and the interaction terms did not meet significance (p = 0.083 and p = 0.21). No differences in pre-CRT total or large CD326 EVs were identified between patients with or without surgical resection (p = 0.72 and p = 0.83). CA19-9 levels were not predictive of clinical outcomes in this trial and showed no association with EV levels. Conclusions: We present an association between clinical outcomes and tdEVs in PDAC, highlighting their potential as a novel, independent biomarker. If validated, tdEVs may offer an opportunity to personalize care and improve risk stratification.

e14626 Background: Despite advancements in neoadjuvant therapy and surgical techniques, patients with pancreatic ductal adenocarcinoma (PDAC) often experience high recurrence rates after total neoadjuvant therapy (TNT) and resection. Existing tumor response scoring systems, such as the College of American Pathologists (CAP) guidelines, fail to stratify recurrence risk effectively, emphasizing the need for novel, objective approaches. Methods: This study evaluated spatial characteristics of residual cancer and stroma in a multi-site cohort of 203 patients with resected PDAC following TNT, not having achieved a major pathologic response (CAP 2 or 3). Whole slide images (WSI) of hematoxylin and eosin-stained sections were digitized and analyzed using artificial intelligence to perform tissue segmentation. Metrics quantifying spatial morphology and configuration, inspired by principles of landscape ecology, were computed to explore spatial features of the tumor microenvironment (TME). Statistical models (recursive partitioning and regression tree [RPRT] model and Cox model with backwards eliminations) incorporating these features were developed to stratify recurrence risk. Results: Five metrics were used for risk classification model build. Three models successfully classified patients into high- and low-risk groups based on spatial metrics. Models demonstrated moderate discriminatory power (C-statistics: 0.560–0.566) and showed that increased tumor fragmentation, like stromal patch density and cancer patch shape index, were strongly associated with improved outcomes. Conclusions: Integrating spatial metrics of cancer-stroma configuration enhances risk stratification beyond conventional scoring systems. Given increased tumor fragmentation is associated with improved outcomes, these data indirectly suggest ecological mechanisms of treatment resistance. This approach offers a pathway toward personalized post-operative management strategies to improve outcomes in PDAC. Risk classification models. Model Description Event/n Median DFS (95% CI) HR(95% CI) p-value C-statistics 1(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 87 / 101 8.22(5.72 – 10.1) 1.64(1.20 – 2.25) 0.002 0.562 Low risk: log standard deviation of the stroma shape index ≤ 0.651 71 / 102 11.90(9.24 – 25.1) Ref 2(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 & mean cancer shape index ≤ 1.775 58 / 63 7.23(5.19 – 9.63) 1.82(1.31 – 2.52) 0.0003 0.560 Low risk: (log standard deviation of the stroma shape index > 0.651 & mean cancer shape index > 1.775) or log standard deviation of the stroma shape index ≤ 0.651 100 / 140 11.57(9.24 – 17.92) Ref 3(Backwards elimination) High risk: PI * > median 86 / 101 8.02(6.08 – 9.86) 1.73(1.26 – 2.37) 0.0007 0.566 Low risk:PI * ≤ median 72 / 102 15.72(9.97 – 25.05) Ref *PI: (0.2701 * log mean stroma area) + (0.3905 * log edge density).

Objectives: Pancreatic neuroendocrine tumors (pNETs) are uncommon, comprising 3%–7% of pancreatic tumors. With increasing incidence due to advanced imaging techniques, there is a need for detailed characterization of these tumors. This study aims to describe the clinical features, diagnostic evaluations, and pathology characteristics of pNETs in a large cohort from a single tertiary center, and to compare these findings with other larger cohort studies. Methods: We conducted a retrospective analysis of 866 patients with pNETs who underwent surgical resection at Mayo Clinic, Rochester, from March 2000 to December 2019. Data on demographics, clinical presentation, laboratory tests, imaging, and pathology were extracted and analyzed. Descriptive statistics were used to summarize the data. Results: The cohort had a median age of 57 years. Nonfunctional tumors were much more prevalent (77.5%), with functional tumors primarily being insulinomas (75.9%). Common presenting symptoms included gastrointestinal (45.3%) and nongastrointestinal symptoms (30.7%). Chromogranin A levels were elevated in 57.5% of patients. Imaging revealed enhancing lesions in most cases, with computed tomography scans performed in 90.9% of patients. Endoscopic ultrasound (EUS) identified tumors in 98.1% of cases, with EUS-FNA showing a sensitivity of 82%. Ki-67 index, used in 58.1% of cases, indicated grade 2 tumors as the most common (55.9%). Metastasis was observed in 39.4% of patients at the time of diagnosis, predominantly in the liver. Conclusion: This study provides a comprehensive description of pNET characteristics in a large surgical cohort. Findings highlight the predominance of nonfunctional tumors and the importance of imaging and EUS in diagnosis. The data can aid in inter-institutional comparisons and enhance understanding of pNETs, contributing to improved patient management and future research.

Abstract Background: Targeted agents, such as nanobodies, can be covalently linked to radionuclide chelators for theranostics. Nanobodies are single-domain immunoglobulin variable regions with a short blood half-lives and high specificity. Calreticulin (CALR) is endoplasmic reticular (ER) protein that translocates to the cell surface upon induction with certain agents, namely doxorubicin. We hypothesized surface CALR on tumors could be targeted using a novel nanobody after induction with doxorubicin. Methods: Studies were conducted using in vitro and in vivo models of pancreatic ductal adenocarcinoma (PDAC). Surface CALR expression was determined by western blot on fractionated lysates, flow cytometry, or immunofluorescence (IF). Doxorubicin was administered by intraperitoneal injection to mouse models of PDAC. The anti-CALR nanobody was developed via the panning of a synthetic nanobody phage display library. The top CALR-binding nanobody was confirmed by ELISA, immunohistochemistry (IHC), and flow cytometry. For in vivo studies, the nanobody was conjugated with the chelator NOTA and radiolabeled with 64Cu. PET images were acquired 1-hour after injection. Significance was determined using an independent t-test and p values &lt; 0.05 were considered statistically significant. Results: PDAC cells treated with doxorubicin showed peak surface CALR expression between 24 and 48 hours. Flow cytometry revealed that 61.7% of live PDAC cultured cells treated for 24 hours expressed surface CALR, compared to 2.2% in controls (p &lt; 0.001). In a subcutaneous syngeneic PDAC mouse model, two doses of doxorubicin 48 hours apart significantly increased surface CALR, with 30.4% of live-cells expressing CALR in treated tumors versus 1.5% in controls (p &lt; 0.001). Similarly, doxorubicin-treated tumors from a PDAC patient derived xenograft (PDX) model had a 6.7-fold increase in live-cells expressing surface CALR compared to controls (p &lt; 0.01). IF confirmed a 1.4-fold increase in CALR expression in doxorubicin-treated tumors from the subcutaneous PDAC mouse model (p &lt; 0.01) and a 1.7-fold increase in the PDAC PDX model (p &lt; 0.01). Our anti-CALR nanobody has an apparent 37 nM affinity for recombinant CALR and successfully detected surface CALR by IHC and flow cytometry. In vivo PET imaging showed significant increased uptake of the 64Cu-NOTA-nanobody in tumors from doxorubicin-treated mice over controls, with no significant changes in heart, kidney, or liver accumulation by PET quantification and flow cytometry (p &lt; 0.05). Conclusions: Systemic doxorubicin treatment induces CALR surface translocation in tumors that can be successfully targeted with a radiolabeled nanobody for PET imaging. Targeting chemotherapy-induced surface CALR using our radiolabeled nanobody could provide a theragnostic strategy for the detection and treatment of PDAC. Citation Format: Rachael Guenter, Yuvasri Golivi, Lucinda Hall, Amro Abdelrahman, Chloe La Prairie, Caleb Miller, Tejeshwar C. Rao, Danielle M. Carlson, Mark J. Truty, Benjamin Larimer, J. Bart Rose. Detection of tumor cell surface calreticulin after doxorubicin treatment using a novel radiolabeled nanobody [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 574.

Abstract Background: Cholangiocarcinoma (CCA) is a diverse type of cancer. For several years, platinum-based chemotherapy (Gemcitabine and Cisplatin) has been the standard treatment and serves as the foundation for current immunotherapy combinations. The typical response rates are generally between 20% and 30%. In this study, we present the results of preclinical efficacy trials conducted on CCA patient-derived xenograft (PDX) models, correlating them with the baseline multiomics profiling of these models to identify molecular characteristics linked to treatment response. Methods: Tumor samples from twenty-eight CCA PDX were used to isolate RNA and protein to be included in multiomics analysis (Phosphoproteomics, Proteomics, and Transcriptomics). A subset of PDX models were treated with various doses of Gemcitabine and Cisplatin. The Median Efficacy Index (MEI) was calculated as the ratio of the difference in median of delta in tumor volume at the last day of the study between the treatment and the control arms divided by the median of delta in tumor volume of the control at the last day of the treatment (positive= growth, negative=inhibition, and 0=no efficacy). A Hierarchical All-Against-All algorithm was utilized to compare multiomics features to the MEI and pathway analysis completed for identified molecular features. Pathway enrichment was calculated using the KEGG database, and random-forest variable selection chose the pathways most influential in predicting sensitivity or resistance. Results: After conducting preclinical efficacy trials, the 11 tested CCA PDX models were classified into sensitive, moderately sensitive, and resistant models based on the MEI on each treatment arm. The top up-regulated genes were identified for Gemcitabine response (SRPK2, GLK2, and RIOK2) and Cisplatin response (NTRK1, PRPF4B, and SRPK2). The top up-regulated genes related to Gemcitabine resistance were NTRK1, PIP5K1C, and CSNK2A2, while those related to Cisplatin resistance were SRPK1, PRPF4B, and CSNK2A1. Downregulation of the anti-folate resistance pathway is a strong predictor of gemcitabine sensitivity, and the lysine degradation pathway is a strong predictor of cisplatin sensitivity. Conclusion: Multiomics signatures of cholangiocarcinoma can forecast responses to standard chemotherapy in preclinical models. New markers of resistance to Gemcitabine and Cisplatin were discovered, linked to NTRK1, SRPK1, and SRPK2. These resistance profiles will be tested using suitable targeted therapies on the resistant CCA patient-derived xenograft (PDX) models. Citation Format: Amro M. Abdelrahman, Danielle M. Carlson, Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Isaac Lynch, Alessandro Fogliati, Stella Konadu Adjei Antwi, Aushinie Abeynayake, Akhilesh Pandey, Mark J. Truty, Gregory J. Gores, Rory L. Smoot. Baseline multiomics and chemotherapy efficacy in preclinical trials using patient-derived xenograft models of cholangiocarcinoma can reveal distinct signatures of chemotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6762.

Abstract Introduction: Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor survival outcomes and multiple mechanisms of resistance to targeted therapies. We previously validated NXP800, a novel GCN2 small-molecule activator, as an effective anti-cancer drug in six CCA cell lines. In vitro testing identified upregulation of phosphorylated eIF2a, as well as upregulation of ATF4, in CCA cells treated with of NXP800 compared to vehicle, suggesting that treated cells led to stimulation of stress-induced genes and subsequently, apoptosis. Furthermore, patient derived xenografts (PDX) models treated with NXP800 were found to have decreased tumor size, particularly in models containing ARID1A, FGFR1, and ATM mutations. Here, we aimed to predict response to NXP800 using sensitivity and resistance signatures from our profiled PDX models. Methods: GCN2 activation in multiple previously profiled PDX models was evaluated. Whole Exome Sequencing (WES) was done on multiple PDX models to characterize the mutational signature and activity in CCA. Two additional CCA xenografts were expanded into the flank of NOD/SCID mice. Tumor bearing mice were treated with NXP800 (35 mg/kg) or vehicle via oral gavage for 28 days once tumors averaged 120 mm3. Tumor volume and animal weight were collected twice weekly. Median Efficacy Index (MEI) scores were generated as drug response rates based on tumor growth. Predicted sensitivity and resistance kinase signatures to predict drug response were produced using multi-omics. Results: Treatment with NXP800 was associated with a statistically significant decrease in tumor size in two additional PDX models, which included ARID1A and BRAF mutants. Preliminary multi-omics on the PDX models revealed that sensitivity mechanisms were upregulated most in EGFR signaling, while resistance signatures were focused around CSNK2A1 signaling. Conclusion: Multi-omics analysis revealed a sensitivity mechanism structured around EGFR signaling, providing insights into which patients should be selected for treatment with NXP800. A Phase 1b clinical trial investigating the effects of NXP800 use in patients with advanced CCA is open and currently accepting patients. Citation Format: Danielle Marie Carlson, Hendrien Kuipers, Amro Abdelrahman, Erik Jessen, Joshua Roldan Kalill, Jennifer Tomlinson, Jack Sample, Akhilesh Pandey, Mark Truty, Rory Smoot. Modeling response prediction to a novel GCN2 kinase activator (NXP800) in patient-derived xenograft cholangiocarcinoma models using a multi-omics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5592.

Abstract Despite advances in surgery and chemotherapy, patients with resectable pancreatic ductal adenocarcinoma (PDAC) face high recurrence rates (40-60%). Total neoadjuvant therapy (TNT) seeks to enhance margin-negative resections (R0) and address micrometastatic disease, with potential survival benefits. Yet, TNT still results in high recurrence rates, with 1-, 2-, and 3-year disease-free survival (DFS) at 65%, 48%, and 32%, respectively. Residual cancer extent is a key prognostic factor in PDAC after TNT. Tumor response scores show favorable outcomes only for patients achieving near-complete or complete pathologic response. In a cohort of 511 PDAC patients treated with TNT and surgery (2015-2022), improved DFS was only found in those with a complete pathologic response (pCR, 7.8%). This highlights the limited use of current response scoring in stratifying post-TNT patient risk, underscoring the need to better understand disease persistence and relapse. Here we developed a quantitative method to assess residual cancer. Using a digital pathology H&E library from 2180 PDAC cases (2000-2022), an AI platform segmented tissue into cancer epithelium, stroma, and normal regions. For 202 TNT-treated cases without pCR, we calculated a ratio of cancer area to stromal area from representative slides. Modeled after the histologic tumor regression grade (HTRG) system, cases were classified into &lt;5%, 5-50%, or &gt;50% cancer-to-stroma ratios. However, this measure alone showed no difference in DFS, suggesting tumor burden cannot fully explain differences in clinical outcomes. We hypothesized that the spatial organization of the tumor microenvironment (TME) is a determinant of patient outcomes. Drawing from landscape ecology, which studies spatial properties of ecosystems, we applied similar principles to analyze tissue architecture. Using an AI-based two-class segmentation of whole-slide images, we assessed spatial features like patch area, perimeter, density, and Shannon diversity. Python’s ‘pylandstats’ package calculated 21 spatial metrics, reduced via stepwise Cox regression using the Akaike information criterion (AIC). The final model included mean Shannon diversity, mean and standard deviation of stromal patch areas, and stromal patch density. Kaplan-Meier analysis of 202 samples stratified model predictions into quartiles, correlating with high (mean DFS 10.85 months), medium (19.32 months), and low risk (25.92 months) groups. These findings aligned with model expectations, underscoring the potential of spatial TME analysis to predict outcomes. Citation Format: Andrea V. Maraone, Zafar Siddiqui, Luis H. Cisneros, Merih D. Toruner, Mark Truty, Cornelius Thiels, Robert R. McWilliams, Steven R. Alberts, Rondell P. Graham, Carlo Maley, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Tumor spatial configuration predicts pancreatic cancer recurrence risk after total neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5070.